Wireless sensor network (WSN) has the feature of low-cost, strong fault tolerance, rapid deployment, no fixed network support, and the capability of taking long-term monitoring tasks, so, WSN has wide application prospects in the field of disaster prediction, rescue, military, environmental, medical, and commercial, etc. Due to the limitations of the sensor node energy, storage and processing capacity, WSN puts forward many challenging research topics for science and technology workers. It has very important significance to study data aggregation and cross-layer optimization problem for the basic theory, supporting technology and application of WSN.This article introduces research status of data aggregation and cross-layer optimization, and describes the basic principles of data aggregation and the implementation method of cross-layer optimization technique carefully. Considering wireless sensor networks for monitoring applications, the paper focuses on the problem of data aggregation and cross-layer optimization in sensor networks, aiming at high energy efficiency, low data aggregation delay and long network lifetime, and proposes energy efficient, easy engineering solution.Considering the problem of the collision conflicts in periodic monitoring application among wireless sensor network nodes, based on the collision avoidance, a data aggregation algorithm is proposed. The algorithm distributively determines aggregators and constructs a data aggregation tree according to the residual energy of nodes, node degree and transmission distance. Using the topology structure of data aggregation tree, the sensing region of the dominators in the tree is divided into four equal areas, thereby the size of collision domains is reduced. The scheduling of nodes in different areas is performed in parallel by the aggregators. The proposed algorithm effectively extends the lifetime of the network and reduces data aggregation delay and has lower complexity.For static data aggregation strategy that canâ€™t adapt to the network structure, data correlation and transmission overhead change, a single path aggregation routing algorithm is proposed. In the proposed algorithm, decision-making mechanism for data aggregation is embedded in the selection process of the dynamic routing to maximize the network lifetime. Then, the problem of maximizing the network lifetime is formulated as a non-convex optimization problem, which includes both real and combinatorial variables. The approximate optimal relay transmission rate and data aggregation routing are obtained by utilizing the dual decomposition method. The presented algorithm effectively guarantees the minimum total energy consumption of the system and prolongs the network lifetime.Because single path data aggregation routing does not take into account the load balancing problem, leading to uneven energy consumption of network nodes, network coverage and connectivity, and reducing the network lifetime, a multi-path data aggregation routing algorithm is proposed. The algorithm establishes a new multi-path routing to maximize network lifetime based on data aggregation strategy and allows the aggregated data and raw data to be transmitted through multiple paths. The multi-path data aggregation routing is designed based on linear programming method. The algorithm obtains the approximate optimal aggregated data rate and the raw data rate through the use of optimization theory and methods. The proposed algorithm not only reduces the energy consumption overhead of the network, balances the node energy consumption and improves the network lifetime.In wireless sensor network for monitoring applications, under a realistic interference based communication model, when node transmits data, it will interfere with other nodes within the interference range, which causes the signal interference and makes these nodes can not correctly receive the data. To solve the problem, an algorithm combined transmission rate allocation of aggregated data with power control is proposed. First, the problem of network lifetime optimization is modeled as a non-convex optimization problem. Then, the non-convex optimization problem is transformed into a convex optimization problem by logarithmic conversion method. Finally, using the optimization theory and methods, the optimization problem is decomposed into two sub-problems:the transmission rate allocation problem and the power control problem. The joint optimization problem is solved by the distributed solution. It is proved the proposed distributed algorithm statistically converges to the global optimal solution. The simulation results show that the combination of transmission rate allocation of aggregated data and power control algorithm can be iterated to find the optimal solutions to maximize the network lifetime.Considering the impact of MAC layer access on the network lifetime, an algorithm is designed to maximize the network lifetime combined aggregated data transmission rate allocation, power control and random access. First, the problem of cross-layer network lifetime optimization is modeled as an optimization problem. Then, the original problem and the corresponding dual problem are gotten by using the Lagrangian theory and relaxation of corresponding restriction condition. Finally, the dual problem is decomposed into the data aggregated data transmission rate allocation problem, power control and random access control problem and put forward distributed algorithms of joint optimization. Simulation results show that the aggregated data rate and link transmission power and the access probability iteratively convergence to the global optimum value, and further extend the network lifetime. |